Abstract
Ant Colony Optimization (ACO) has been commonly applied in solving discrete optimization problems. This is an attempt to apply ACO in a dynamic environment for finding the optimal route. To create a dynamically changing scenario, in addition to distance, constraints such as air quality, congestion, user feedback, etc are also incorporated for deciding the optimal route. Max-Min Ant System (MMAS), an ACO algorithm is used to find the optimal path in this dynamic scenario. A local search parameter ε is also introduced in addition to ρ to improve the exploration of unused paths. Adaptability was studied by dynamically changing the costs associated with different parameters.
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